•March 2020 stock market crash triggered by COVID-19.•Natural gas, food, healthcare, and software stocks earn high positive returns.•Petroleum, real estate, entertainment, and hospitality stocks fall ...dramatically.•Loser stocks exhibit extreme asymmetric volatility.•Differential reaction of poorest performers to COVID-19.
This paper investigates the US stock market performance during the crash of March 2020 triggered by COVID-19. We find that natural gas, food, healthcare, and software stocks earn high positive returns, whereas equity values in petroleum, real estate, entertainment, and hospitality sectors fall dramatically. Moreover, loser stocks exhibit extreme asymmetric volatility that correlates negatively with stock returns. Firms react in a variety of different ways to the COVID-19 revenue shock. The analysis of the 8K and DEF14A filings of poorest performers reveals departures of senior executives, remuneration cuts, and (most surprisingly) newly approved cash bonuses and salary increases.
To evaluate the accuracy of the measurement of the ganglion cell layer (GCL) of the posterior pole analysis (PPA) software of the Spectralis spectral-domain (SD) optical coherence tomography (OCT) ...device (Heidelberg Engineering, Inc., Heidelberg, Germany), the asymmetry of paired GCL sectors, the total retinal thickness asymmetry (RTA), and the peripapillary retinal nerve fiber layer (pRNFL) test to discriminate between healthy, early and advanced glaucoma eyes.
Three hundred eighteen eyes of 161 individuals with reliable visual fields (VF) were enrolled in this study. All participants were examined using the standard posterior pole and the pRNFL protocols of the Spectralis OCT device. VF impairment was graded in hemifields, and the GCL sectors were correlated with this damage. Thicknesses of each GCL, the GCL map deviation asymmetry and the pRNFL were compared between control and glaucomatous eyes. The area under the receiver operating characteristic curve (AUC) of these analyses was assessed.
Fourteen of the 16 sectors of the GCL and pRNFL were significantly thinner in eyes with glaucoma than in control eyes (p<0.006). Similarly, the GCL map deviation showed a significant difference between these eyes and both the control eyes as well as the eyes with early glaucoma (p = 0.001 and p = 0.039, respectively). The highest values of AUC to diagnose both early and advanced glaucoma corresponded to the average pRNFL analysis and the GCL map deviation (AUC>0.823, p<0.040 and AUC>0.708, p<0.188, respectively).
Although 16 central sectors of the GCL observed with PPA showed good correlation with VF damage, the pRNFL and the GCL map deviation were more effective for discrimination of glaucomatous damage.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
In cancer research, it is important to classify tissue samples in different classes (normal, tumour, tumour type, etc.). Gene selection purpose is to find the minimum number of genes that can predict ...sample classes with efficacy. This work is focused on the gene selection problem by introducing a new hybrid method. This new method combines a first step of gene filtering with an optimization algorithm in a second step to find the best subset of genes for the classification task. The first step uses the Analytic Hierarchy Process, in which five ranking methods are used to select the most relevant genes in the dataset. In this way, this gene filtering reduces the number of genes to manage. Regarding the second step, the gene selection can be divided into two objectives: minimizing the number of selected genes and maximizing the classification accuracy. Therefore, we have used a multi-objective optimization approach. More exactly, an Artificial Bee Colony based on Dominance (ABCD) algorithm has been proposed for this second step. Our approach has been tested with eleven real cancer datasets and the results have been compared with several multi-objective methods proposed in the scientific literature. Our results show a high accuracy in the classification task with a small subset of genes. Also, to prove the relevance of our proposal, a biological analysis has been developed on the genes selected. The conclusions of this biological analysis are positive, because the selected genes are closely linked to the cancer dataset they belong to.
•A new hybrid method for gene selection has been developed.•The method combines AHP-based gene filtering, SVM, and ABCD algorithm.•The evaluation has been performed with 11 cancer datasets of 7 types of cancer.•Comparisons have been done with 7 multi-objective methods from other authors.•The biological relevance has been evaluated, showing the good results of the proposal.
Identification of biomarkers is essential for the diagnosis and prognosis of certain diseases, like cancer. Gene selection purpose is finding the minimum number of genes that can classify a (e.g. ...normal or tumour) sample with a high accuracy. Therefore, the selected genes can be studied as potential cancer biomarkers. In this article, a new method for gene selection is proposed in two steps. The first step is a filtering of the most relevant genes of a gene expression dataset. In this step, three feature selection methods have been combined. Since gene selection is a two-objective problem (minimizing the number of selected genes while maximizing the classification accuracy), the second step is performed as a multi-objective optimization, using an Artificial Bee Colony based on Dominance (ABCD) algorithm. ABCD algorithm uses internally a support vector machine (SVM) classifier. The method has been tested with five RNA-seq cancer datasets and with a comparative study of the results obtained by the method and by other five methods proposed in the scientific literature by other authors. Finally, in order to check if the genes selected by the proposed method could be studied as biomarkers, the relation between the selected genes and the cancer they belong to is analysed. It can be concluded that the proposed method is effective in gene selection for the identification of cancer biomarkers from RNA-seq data.
•A new gene selection method for cancer biomarker identification has been developed.•The method combines feature selection, SVM, and ABCD algorithm.•The evaluation has been performed with five RNA-seq cancer datasets.•Comparisons have been done with five gene selection methods from other authors.•The biological relevance has been analysed, showing the good results of the proposal.
Currently, autonomous robotics is one of the most interesting and researched areas of technology. At the beginning, robots only worked in the industrial sector but, gradually, they started to be ...introduced into other sectors such as medicine or social environments becoming part of society. In mobile robots, the path planning (PP) problem is one of the most researched topics. Taking into account that the PP problem is an NP-hard problem, multi-objective evolutionary algorithms (MOEAs) are good candidates to solve this problem. In this work, a new multi-objective approach based on the flashing behavior of fireflies in nature, the multi-objective firefly algorithm (MO-FA), is proposed to solve the PP problem. This proposed algorithm is a swarm intelligence algorithm. The proposed MO-FA handles three different objectives to obtain accurate and efficient solutions. These objectives are the following: the path safety, the path length, and the path smoothness (related to the energy consumption). Furthermore, and to test the proposed MOEA, we have used eight realistic scenarios for the path’s calculation. On the other hand, we also compare our proposal with other approaches of the state of the art, showing the advantages of MO-FA. In particular, to evaluate the obtained results we applied specific quality metrics. Moreover, to demonstrate the statistical evidence of the obtained results, we also performed a statistical analysis. Finally, the study shows that the proposed MO-FA is a good alternative to solve the PP problem.
The protein-protein interaction (PPI) network alignment has proven to be an efficient technique in the diagnosis and prevention of certain diseases. However, the difficulty in maximizing, at the same ...time, the two qualities that measure the goodness of alignments (topological and biological quality) has led aligners to produce very different alignments. Thus making a comparative study among alignments of such different qualities a big challenge. Multi-objective optimization is a computer method, which is very powerful in this kind of contexts because both conflicting qualities are considered together. Analysing the alignments of each PPI network aligner with multi-objective methodologies allows you to visualize a bigger picture of the alignments and their qualities, obtaining very interesting conclusions. This paper proposes a comprehensive PPI network aligner study in the multi-objective domain.
Alignments from each aligner and all aligners together were studied and compared to each other via Pareto dominance methodologies. The best alignments produced by each aligner and all aligners together for five different alignment scenarios were displayed in Pareto front graphs. Later, the aligners were ranked according to the topological, biological, and combined quality of their alignments. Finally, the aligners were also ranked based on their average runtimes.
Regarding aligners constructing the best overall alignments, we found that SAlign, BEAMS, SANA, and HubAlign are the best options. Additionally, the alignments of best topological quality are produced by: SANA, SAlign, and HubAlign aligners. On the contrary, the aligners returning the alignments of best biological quality are: BEAMS, TAME, and WAVE. However, if there are time constraints, it is recommended to select SAlign to obtain high topological quality alignments and PISwap or SAlign aligners for high biological quality alignments.
The use of the SANA aligner is recommended for obtaining the best alignments of topological quality, BEAMS for alignments of the best biological quality, and SAlign for alignments of the best combined topological and biological quality. Simultaneously, SANA and BEAMS have above-average runtimes. Therefore, it is suggested, if necessary due to time restrictions, to choose other, faster aligners like SAlign or PISwap whose alignments are also of high quality.
•Comprehensive survey of a large number of protein interaction network aligners.•Analysis, from a multi-objective perspective, of all alignments of all aligners.•Study of the ability of each network aligner to produce non-dominated alignments.•Ranking of aligners according to the topological, biological, and combined quality.•Ranking of protein interaction network aligners on the basis of their execution times.
The application of bacteriophages as antibacterial agents has many benefits in the "post-antibiotic age". To increase the number of successfully targeted bacterial strains, phage cocktails, instead ...of a single phage, are commonly formulated. Nevertheless, there is currently no consensus pipeline for phage cocktail development. Thus, although large cocktails increase the spectrum of activity, they could produce side effects such as the mobilization of virulence or antibiotic resistance genes. On the other hand, coinfection (simultaneous infection of one host cell by several phages) might reduce the potential for bacteria to evolve phage resistance, but some antagonistic interactions amongst phages might be detrimental for the outcome of phage cocktail application. With this in mind, we introduce here a new method, which considers the host range and each individual phage-host interaction, to design the phage mixtures that best suppress the target bacteria while minimizing the number of phages to restrict manufacturing costs. Additionally, putative phage-phage interactions in cocktails and phage-bacteria networks are compared as the understanding of the complex interactions amongst bacteriophages could be critical in the development of realistic phage therapy models in the future.
Mixed-linkage glucan (MLG) is a cell wall polysaccharide containing a backbone of unbranched (1,3)-and (l, 4)-linked β-glucosyl residues. Based on its occurrence in plants and chemical ...characteristics, MLG has primarily been associated with the regulation of cell wall expansion due to its high and transient accumulation in young, expanding tissues. The Cellulose synthase-like F (CslF) subfamily of glycosyltransferases has previously been implicated in mediating the biosynthesis of this polymer. We confirmed that the rice (Oryza sativa) CslF6 gene mediates the biosynthesis of MLG by overexpressing it in Nicotiana benthamiana. Rice cslf6 knockout mutants show a slight decrease in height and stem diameter but otherwise grew normally during vegetative development. However, cslf6 mutants display a drastic decrease in MLG content (97% reduction in coleoptiles and virtually undetectable in other tissues). Immunodetection with an anti-MLG monoclonal antibody revealed that the coleoptiles and leaves retain trace amounts of MLG only in specific cell types such as sclerenchyma fibers. These results correlate with the absence of endogenous MLG synthase activity in mutant seedlings and 4-week-old sheaths. Mutant cell walls are weaker in mature stems but not seedlings, and more brittle in both stems and seedlings, compared to wild type. Mutants also display lesion mimic phenotypes in leaves, which correlates with enhanced defense-related gene expression and enhanced disease resistance. Taken together, our results underline a weaker role of MLG in cell expansion than previously thought, and highlight a structural role for MLG in nonexpanding, mature stem tissues in rice.
The alignment of protein–protein interaction (PPI) networks is in the spotlight of the research in bioinformatics. The main goal is to find structural or functional complexes that are evolutionarily ...conserved between species. Recent works in the area struggle to produce alignments of both good topological and functional quality, since those two objectives conflict more than expected. To this end, we introduce a decomposition-based multi-objective algorithm with two new problem-aware mutation operators, being each of them focused on the improvement of one of both objectives. The experiments have been performed over 10 scenarios of PPI network alignment with real data from five different species. The results have been evaluated with five quality metrics. The performed experiments have confirmed that the solutions resulting from our proposal obtain statistically significant improvements and are of higher quality than the solutions from related works.
•The protein–protein interaction network alignment problem is addressed.•Proposal of a new multi-objective approach based on decomposition to solve it.•Development of two new problem-aware mutation operators improving the alignments.•Construction of alignments for 10 scenarios, involving real data from five species.•Comparison with multi-objective and biological tools, obtaining better results.
There exist a number of satellites on different earth observation platforms, which provide multispectral images together with a panchromatic image, that is, an image containing reflectance data ...representative of a wide range of bands and wavelengths. Pansharpening is a pixel-level fusion technique used to increase the spatial resolution of the multispectral image while simultaneously preserving its spectral information. In this paper, we provide a review of the pan-sharpening methods proposed in the literature giving a clear classification of them and a description of their main characteristics. Finally, we analyze how the quality of the pansharpened images can be assessed both visually and quantitatively and examine the different quality measures proposed for that purpose.
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